A DNC trained on the traversal task with 256 memory locations was tested while varying the number of memory locations and graph triples. The heat map shows the fraction of traversals of length 1–10 performed perfectly by the network, out of a batch of 100. There is a clear correspondence between the number of triples in the graph and the number of memory locations required to solve the task, reflecting our earlier analysis (Fig. 3) that suggests that DNC writes each triple to a separate location in memory. The network appears to exploit all available memory, regardless of how much memory it was trained with. This supports our claim that memory is independent of processing in a DNC, and points to large-scale applications such as knowledge graph processing.